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The upcoming revolution in data science: Expansion in research by intelligence, econometrics and forecasting.



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The research areas of data science are evolving rapidly. In this article, we take at a look at the most prominent ones. Since most AI algorithms favor a distributed architecture, quantum computers will be vital for the development of AI. We’re already seeing large investments from companies like Google, IBM, Intel, HP and many others into creating faster quantum computers which can be used not only for research but also for consumer grade electronics(although this will take a few years)

IT systems

The design and management of IT systems have a prominent effect in determining the success of an organization. So, there needs to be a definite strategy in managing these systems. The capabilities of IT systems also determine the extent to which analytics would be deployed by the organization. The major facets of Information Technology systems include design, development,  and outsourcing. Numerous application areas correspond to the information systems in question. These include education system, logistics, healthcare, supply chain management, business management, and social media campaigns.

Data mining

One of the most prominent areas which determine the success of a business analytical strategy is data mining. Data mining can be referred to as a process that extracts a large number of data sets and examines various patterns and relationships between these clusters. In this way, data mining plays a very adaptive role in predictive analytics in the domain of big data management. Data mining also boosts the research capabilities of an organization.

Research in the domain of data mining focuses on the collection of data, cleansing, management, and analytical capabilities. The application areas of data mining in general and text mining, in particular, include business marketing, operations management, cybersecurity, healthcare, cybercrime, design, and development of grammatical tools.


The development of empirical models related to economics and business management is known as econometrics. Econometrics relies on model development with the help of time series forecasting. It relies on various mathematical models and estimation techniques including Bayesian learning. A major focus of econometrics is the improvement of the performance and efficiency of computing models. The application areas of econometrics include online testing platforms for a business analytics course, operation management, and financial modeling for research purposes.


Forecasting is an extension of econometrics because these two tools work in consonance with each other to predict the future business development strategies of an organization. The development of sound and accurate forecasts based on previous data is a major aim of forecasting.


The information which is gathered from previous models using data analytics can be employed to develop and optimize effective business solutions. Business intelligence leads to optimization of business processes which helps in effective decision making. Optimization techniques in business development and analytics are classified into the subcategories of descriptive and preemptive Analytics. The Optimisation techniques and models in business development support various decision management systems in the long run. Optimization strategies also help in conceiving dynamic environments which help in the development of specialized business portfolios.

Concluding remarks

The ocean of data science is expanding and so are the research areas associated with this field witnessing a steep rise.

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